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Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation

Abstract

New technologies to record electrical activity from the brain on a massive scale offer tremendous opportunities for discovery. Electrical measurements of large-scale brain dynamics, termed field potentials, are especially important to understanding and treating the human brain. Here, our goal is to provide best practices on how field potential recordings (electroencephalograms, magnetoencephalograms, electrocorticograms and local field potentials) can be analyzed to identify large-scale brain dynamics, and to highlight critical issues and limitations of interpretation in current work. We focus our discussion of analyses around the broad themes of activation, correlation, communication and coding. We provide recommendations for interpreting the data using forward and inverse models. The forward model describes how field potentials are generated by the activity of populations of neurons. The inverse model describes how to infer the activity of populations of neurons from field potential recordings. A recurring theme is the challenge of understanding how field potentials reflect neuronal population activity given the complexity of the underlying brain systems.

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Fig. 1: Field potential recording modalities.
Fig. 2: Forward models.
Fig. 3: Time series and spectral estimation.
Fig. 4: Current source density analysis.
Fig. 5: Granger-causal inferences.
Fig. 6: Phase-dependent neuronal coding.

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Acknowledgements

C.S. acknowledges Y. Kajikawa for contributing figure 4b and for editorial comments. C.S. acknowledges grant support from MH111439 and DC015780. G.E. acknowledges grant support from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270 (Human Brain Project SGA1). P.F. acknowledges grant support from DFG (SPP 1665, FOR 1847, FR2557/5-1-CORNET), the European Union (FP7-600730-Magnetrodes), NIH (1U54MH091657-WU-Minn-Consortium-HCP), and LOEWE (NeFF). W.T. acknowledges grant support from NIH-NINDS R01NS079533, U.S. Department of Veterans Affairs, Merit Review Award RX000668, and the Pablo J. Salame ’88 Goldman Sachs endowed Assistant Professorship of Computational Neuroscience. B.P. acknowledges grant support from NEI R01-EY024067, NINDS R01-NS104923, ARO MURI 68984-CS-MUR, NSF BCS 150236, and DoD contracts W911NF- 14-2-0043 and N66001-17-C-4002. A.S. acknowledges grant support from BrainCom from EU Horizon 2020 program via grant no. 732032, Munich Cluster for Systems Neurology (SyNergy, EXC 1010), Deutsche Forschungsgemeinschaft Priority Program 1665 and 1392 and Bundesministerium für Bildung und Forschung via grant no. 01GQ0440 (Bernstein Centre for Computational Neuroscience Munich).

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Pesaran, B., Vinck, M., Einevoll, G.T. et al. Investigating large-scale brain dynamics using field potential recordings: analysis and interpretation. Nat Neurosci 21, 903–919 (2018). https://doi.org/10.1038/s41593-018-0171-8

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